In-silico Characterization and 3D Structure Prediction of MX Protein of Lates Calcarifer (Barramundi): A Major Threat to Aqua Industry
Authors
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,IN
Ruby Singh -
,INK. Pani Prasad
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,INAjey Pathak
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,INPrachi Srivastava
DOI:
https://doi.org/10.18311/ti/2018/v25i4/24029Keywords:
Barramundi, Betanoda Virus, Ligand, MX Protein, Viral Nervous NecrosisAbstract
Betanoda virus is one of the most important and emerging groups of viruses known to infect around 40 species found to be worldwide in distribution. The most common and virulent target of infection for this virus is (Lates Calcarifer) (barramundi). It is found that the expression of MX protein is found to be the more susceptible reason for this viral infection. Considering this current study including characterization to structure prediction revolves around the MX protein as a target. The progression of this study describes the amino acid sequence of MX protein was retrieved from UniProt database in Fasta format and further primary structure analysis and characterization including nature of amino acids, instability index reading, GRAVY, determination of phosphorylation as well as signal peptide cleavage sites was done with the help of various tools. Secondary structure prediction has proceeded through SOPMA server analysis revealed that MX protein has mixed secondary structure, i.e., mostly alpha-helix and beta-turn. The progression of this work prediction of a 3D structure along with functional site prediction of MX protein of Fish (Lates Calcarifer) is done through standard modeling tools. The 3D structure of this protein of (Lates Calcarifer) as documented in this study may provide a valuable aid for designing an inhibitor or better ligand against viral nervous necrosis disease and could play a vital role in drug design.
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Accepted 2019-09-05
Published 2019-10-22
References
Xavier Irz, James R. Stevenson, Arnold Tanoy, Portia Villarante, Pierre Morissens. The equity and poverty impacts of aquaculture: Insights from the Philippines, Development Policy Review. 2007; 25(4):495-516. https:// doi.org/10.1111/j.1467-7679.2007.00382.x.
Ayalew Assefa, Fufa Abunna. Maintenance of fish health in aquaculture: Review of epidemiological approaches for prevention and control of infectious disease of fish, Veterinary Medicine International. 2018; 2018(5432497):10.https://doi.org/10.1155/2018/5432497. PMid: 29682272, PMCid: PMC5846361.
Shetty M, Maiti B, Shiva Kumar, Santhosh K, Venugopal MN, Karunasagr I. Betanodavirus of marine and fresh water distribution, Indian J. Virol. 2012; 2:114-23. https:// doi.org/10.1007/s13337-012-0088-x. PMid: 23997435, PMCid: PMC3550751.
Chi SC, Wu YC, Cheng TM. Persistent infection of betanodavirus in a novel cell line derived from the brain tissue of barramundi Lates Calcarifer, Dis. Aquat. Organ.2005; 65:91-98. https://doi.org/10.3354/dao065091. PMid: 16060261.
Wu YC, Chi SC. Cloning and analysis of antiviral activity of a barramundi (Lates calcarifer) Mx gene, Fish and Shellfish Immunol. 2006; 23:97-108. https://doi.org/10.1016/j. fsi.2006.09.008. PMid: 17097891.
Yu-Chi Wu, Yi-Fan Lu, Shau-Chi Chi. Anti-viral mechanism of barramundi Mx against betanodavirus involves the inhibition of viral RNA synthesis through the interference of RdRp, Fish and Shellfish Immunology. 2010; 28:467-75. https://doi.org/10.1016/j.fsi.2009.12.008. PMid: 20034570.
Wu YC, Chi SC. Persistence of betanodavirus in Barramundi Brain (BB) cell line involves the induction of Interferon response, Fish and Shellfish Immunology. 2006; 21(5):5407.https://doi.org/10.1016/j.fsi.2006.03.002. PMid: 16698284.
Chi SC, Shieh JR, Lin SJ. Genetic and antigenic analysis of betanodaviruses isolated from aquatic organisms in Taiwan, Dis. Aquat. Organ. 2003; 55:221-28. https://doi.org/10.3354/dao055221. PMid: 13677508.
Anshul Tiwari, Prachi Srivastava. In silico characterization of retinal s antigen and retinol binding protein, Journal of Ocular Biology, Diseases and Informatics. 2012; 5(2):40-43.
Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook; 2005. p. 571-607. https://doi.org/10.1385/1-59259-890-0:571.
Gill SC, VonHippel PH. Calculation of protein extinction coefficients from amino acid sequence data, Anal. Biochem.1989; 182(2):319-26. https://doi.org/10.1016/00032697(89)90602-7.
Geourjon C, G. Deleage. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments, Comput.Appl. Biosci. 1995; 11:681-84. https://doi.org/10.1093/ bioinformatics/11.6.681.
PirovanoW, Heringa J. Protein secondary structure prediction, Methods Mol. Biol. 2010; 609:327-48. https:// doi.org/10.1007/978-1-60327-241-4_19. PMid:20221928.
Blom N, Gammel S, Brunak S. Sequence and structure based prediction of eukaryotic protein phosphorylation sites, Journal of Molecular Biology. 1999; 294:1351-62. https:// doi.org/10.1006/jmbi.1999.3310. PMid: 10600390.
Petersen TN, Brunak S, Heijne G, Nielsen H. Signal P 4.0: Discriminating signal peptides from transmembrane regions, Nature Methods. 2011; 8:785-86. https://doi.org/10.1038/nmeth.1701. PMid: 21959131.
Eswar N, Eramian D, Webb B, Shen MY, Sali A. Protein structure modeling with MODELLER, Current Protocols in Bioinformatics John Wiley & Sons, Inc. 2006; 15:5.6.15.6.30. https://doi.org/10.1002/0471250953.bi0506s15.PMid: 18428767, PMCid: PMC4186674.
Andres Aszodi, William R. Taylor. Homology modelling by distance geometry: Division of Mathematical Biology, National Institute for medical Research, Folding and Design. 1996; 1:325-34. https://doi.org/10.1016/S13590278(96)00048-X.
Browne WJ, North ACT, Phillips DC, Brew K, Vanaman TC, Hill RL. A possible three-dimensional structure of bovine alpha-lactalbumin based on that of hens eggwhite lysozyme, J. Mol. Biol. 1969; 42:65-86. https://doi.org/10.1016/0022-2836(69)90487-2.
Sali A. Modelling mutations and homologous proteins, Current Opinion Biotechnology. 1995; 6:437-51. https:// doi.org/10.1016/0958-1669(95)80074-3
Dmitrii M, Nikolaev, Andrey A. Shtyrov, Maxim S, Panov, Adeel Jamal, Oleg B. Chakchir, Vladimir A, Kochemirovsky, Massimo Olivucci, Mikhail N. Ryazantsev. A comparative study of modern homology modeling algorithms for rhodopsin structure prediction, ACS Omega. 2018; 3:7555−66. https://doi.org/10.1021/acsomega.8b00721.PMid: 30087916, PMCid: PMC6068592.
Dassault Systems Biovia. Discovery studio modelling environment. Release 2017, San Diego: Dassault Systèmes, 2016.
Ikai AJ. Thermo Stability and aliphatic index of globular proteins, J. Biochem. 1980; 88:1895-98.
Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 1982; 157:105−32. https://doi.org/10.1016/0022-2836(82)90515-0.
Jiangning Song, Huilinwang, Jiaweiwang, Andre, Tatiana, Bingjiao, Ziding, Tatsuya, Geoffrey, Roger. Phosphodirect: A bioinformatics tool for predicting for prediction of human kinase-specific phosphorylation substrates and sites by integrating hetreogenous feature selection, Scientific Reports. 2017; 7:6862. https://doi.org/10.1038/s41598-01707199-4. PMid: 28761071, PMCid: PMC5537252.
Duan G, Walther D. The roles of post-translational modifications in the context of protein interaction networks, Comput. Biol. 2015; 11(2):1004049. https:// doi.org/10.1371/journal.pcbi.1004049. PMid: 25692714, PMCid: PMC4333291.
Huang HD, Lee TY, Tzeng SW, Horng JT. Kinase Phos: A web tool for identifying protein kinase-specific phosphorylation sites, Nucleic. Acids. Res. 2005; 33:226−29. https:// doi.org/10.1093/nar/gki471. PMid: 15980458, PMCid: PMC1160232.
Johnson LN. The regulation of protein phosphorylation, Biochem. Soc. Trans. 2009; 37:627−41. https://doi.org/10.1042/BST0370627. PMid: 19614568.
Mundla Sri Latha, Madhu Sudhana Saddala. Molecular docking based screening of a simulated HIF-1 protein model for potential inhibitors, Biomedical Informatics. 2017; 13(11):388−93. https://doi.org/10.6026/97320630013388. PMid: 29225432, PMCid: PMC5712784.
Shen, Min-yi; Sali, Andrej. Statistical potential for assessment and prediction of protein structures, Protein Science. 2006; 15(11):2507−24. https://doi.org/10.1110/ ps.062416606. PMid: 17075131, PMCid: PMC2242414.
Azad IS, Shekhar MS, Thirunavukkarasu AR, Poornima M, Kailasam M, Rajan JJ, Ali SA, Abraham M, Ravichandran P. Nodavirus infection causes mortalities in hatchery produced larvae of Lates calcarifer: First report from India, Dis. Aquat. Organ. 2005; 63:113−18. https://doi.org/10.3354/dao063113. PMid: 15819426.
Thiery R, Cozien J, Cabon J, Lamour F, Baud M, Schneeman A. Induction of a protective immune response against viral nervous necrosis in the European sea bass dicentrarchus labrax by using betanodavirus-like particles, Journal of Virology. 2006; 80(20):10201−07. https://doi.org/10.1128/ JVI.01098-06. PMid: 17005697, PMCid: PMC1617310.
Anderson ED, Mourich DV, Fahrenkrug SC, La Patra S, Shepherd J, Leong A. Genetic immunization of rainbow trout (Oncorhynchusmykiss) against infectious hematopoietic necrosis virus, Mol. Mar. Biol. Biotechnol.1996; 5:114−22.
Castric J, Thiéry R, Jeffroy J, Kinkelin P, Raymond JC. Sea bream Sparusaurata, an asymptomatic contagious fish host for nodavirus, Dis. Aquat. Organ. 2001; 47:33−38. https:// doi.org/10.3354/dao047033. PMid: 11797913.
Chi SC, Lo BJ, Lin SC. Characterization of Grouper Nervous Necrosis Virus (GNNV). J. Fish Dis. 2001; 24:3−14. https:// doi.org/10.1046/j.1365-2761.2001.00256.x.
Review of OIE international aquatic animal health code, International Journal of Parasitology. 2002; 32(4):487−88.
